Image magnification and reduction using high order filtering on the cell broadband engine

Kidwai, Hashir; Sibai, Fadi N.; Rabie, Tamer

doi:10.1109/ssd.2008.4632837

Cited by 9 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the parallel or embedded (PPE+SPE) version, we used spu_read_decrementer() and spu_write_decrementer() functions to measure the total number of clock ticks and passed that information to the PPE code by updating the counter [3]. These functions return more accurate and reliable timing information compared to the gettimeofday function.…”

Section: ) Experiments and Resultsmentioning

confidence: 99%

“…Eight computational cores with vector capabilities are known as the SPEs (synergistic processing elements). With its multi-core parallelism and vector and SIMD processing capabilities, the STI Cell processor was shown to deliver top computation performance levels on graphics and image processing applications [3] and video surveillance applications [4]. Because of the Cell's unique architecture, shorter simulator run times are expected on the Cell platform compared to existing systems.…”

Section: ) Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallel Orthomin Equation Solver on the Cell Broadband Engine

Sibai

Kidwai

2011

2011 Sixth International Symposium on Parallel Computing in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

This paper presents our parallelization and implementation of the ORTHOMIN solver on the Cell Broadband Engine. The solution of linear systems of equations is one of the most central processing unit-intensive steps in many engineering and simulation applications and can greatly benefit from the multitude of SIMD-capable synergistic processor element (SPE) cores in the Cell processor. We report the serial ORTHOMIN implementation on the Cell's PowerPC processor element (PPE), and the parallelization and performance analysis of ORTHOMIN across 8 SPEs for Tridiagonal (1-D reservoir grid) and Heptadiagonal (3-D reservoir grid) matrices. Our implementation is shown to scale well with data size, and grid dimensionality.Parallel linear equation solvers are used in the solution of many engineering and simulation applications. These solvers find the solution of a system of linear equations taking advantage of parallel processing methods. Parallelism is essential to reduce the execution time for large systems of linear equations with thousands of equations and thousands of unknown variables. In prior work [1-2], we parallelized and implemented the Conjugate Gradient solver on the STI (Sony, Toshiba, IBM) Cell Broadband Engine (Cell) platform [8][9][10]. The Cell processor is a multi-core processor with nine cores on a single chip. One core is a PowerPC RISC processor known as the PPE (power processing element). Eight computational cores with vector capabilities are known as the SPEs (synergistic processing elements). With its multi-core parallelism and vector and SIMD processing capabilities, the STI Cell processor was shown to deliver top computation performance levels on graphics and image processing applications [3] and video surveillance applications [4]. Because of the Cell's unique architecture, shorter simulator run times are expected on the Cell platform compared to existing systems. Shorter simulation times translate into faster solutions (in terms of days, depending on the size of the problem) and allow for more simulation runs to be performed on the same hardware resources during fixed time durations.A key component of a simulator in engineering disciplines is a linear equation solver which computes the solution to a system of difference equations. ORTHOMIN is an example of minimal-residual method used in engineering simulations such as oil reservoir simulation. In ORTHOMIN, a series of vectors is generated for x(1), x(2), …, x(k+1) for k+1 <= n starting from an initial estimate of x(0), where n is the number linear equations or the number of unknown variables (unknowns). The linear system equation is of the form A X = B where X is the vector of unknowns, and A and B are scalar vectors. A is known as the coefficient matrix and X is known as the unknown variable matrix. The exact solution is obtained in n iterations assuming no round off errors occur, while an approximate solution is obtained in less than n iterations. The drawback of ORTHOMIN is that it requires a large memory which provides a huge bottleneck on ...

show abstract

Section: ) Experiments and Resultsmentioning

confidence: 99%

Section: ) Introductionmentioning

confidence: 99%

Parallel Orthomin Equation Solver on the Cell Broadband Engine

Sibai

Kidwai

2011

2011 Sixth International Symposium on Parallel Computing in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…We ran our experiments as in [14] on 1 Cell blade containing two 9-core Cell processor packages. We calculated the number of ticks on the serial PPE-only model through the mftb function which is available through the PPU intrinsic header files along with the gettimeofday function.…”

Section: Methodsmentioning

confidence: 99%

“…Our image resizing algorithm is identical to the bilinear filtering algorithm used in [14] in which we set the value of the zoomed pixel at location (x, y) as follows: The following loop illustrates this bilinear interpolation algorithm on every processing element (SPU in Cell BE and processing node in Blue Gene). [15].…”

Section: Algorithms and Their Implementationsmentioning

confidence: 99%

Image processing applications performance study on Cell BE and Blue Gene/L

El-Moursy

Sibai

2010

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

SUMMARYTwo image processing applications, edge detection and image resizing, are studied in this paper on two HPC platforms namely the Cell BE and the Blue Gene/L machines. In this paper we focus on the performance scalability of the studied applications. Our results show that the scale of the problem to be solved highly affects the fitness of the platform. If the data set size is to fit into the Cell core, the fast on-chip inter-core communication of a multi-core system pays back for its high technology design. On the other hand, the overhead of the distant communication in the massively parallel Blue Gene/L machine will only show its benefits for huge data set size that otherwise mandates multiple round-trip data communications between the local memory of a core and main memory.

show abstract

“…The Cell processor is a multi-core processor with 9 cores on one single chip package. With its multi-core parallelism and SIMD processing capabilities, the IBM Cell processor was shown to deliver top computation performance levels compared to other CPUs on graphics and image processing applications [8] and other scientific computation applications [9]. The Cell is a heterogeneous multi-core chip containing one 64-bit PowerPC Processing Element (PPE), eight specialized co-processors called Synergistic Processing Elements (SPE), and one internal high speed bus called Element Interconnect Bus (EIB) which links PPE and SPEs together.…”

Section: Introductionmentioning

confidence: 99%

Parallel Implementation and Performance Analysis of a 3D Oil Reservoir Data Visualization Tool on the Cell Broadband Engine and CUDA GPU

Siba

Mohammad

Kidwai

et al. 2012

2012 IEEE 14th International Conference on High Performance Computing and Communication &Amp; 2012 IEEE 9th International Confe

Self Cite

View full text Add to dashboard Cite

Usefulness of graphically visualizing and manipulating large data sets in oil and gas exploration and production is as important as ever. This paper describes the development and parallelization of a multi-phase 3D oil-water reservoir visualization tool on the IBM Cell computer and CUDA enabled GPU. An independent Oil reservoir simulator described in [1] was used to generate the pressure and oil / water saturation values over a certain period of time. The oil reservoir visualization tool displays data grids in a 3D environment and allows the user to interact with it. Due to large speed requirements, our aim is to parallelize the computations required to interact with and visualize the grid, mainly transformation [2], zooming, camera movement [3] and compute intensive lighting model [4][5]. This tool also allows the user to playback the simulation results over a time duration and fetches data values upon mouse click at a particular grid point on a particular day. The development environments are nVIDIA CUDA and IBM Cell SDK 3.0 along with QT and OpenGL libraries. Various experiments were run on an x86 computer with nVIDIA Quadro FX 5800 GPU, and on an IBM Cell BE computer with 1 QS20 Cell blade containing two 9-core Cell processor packages. Our results indicate that the nVIDIA GPU provides on average, speed up of 67x over serial implementation and IBM Cell BE with 16 SPE SIMD implementation 32x over the serial implementation.

show abstract

Image magnification and reduction using high order filtering on the cell broadband engine

Cited by 9 publications

References 1 publication

Parallel Orthomin Equation Solver on the Cell Broadband Engine

Parallel Orthomin Equation Solver on the Cell Broadband Engine

Image processing applications performance study on Cell BE and Blue Gene/L

Parallel Implementation and Performance Analysis of a 3D Oil Reservoir Data Visualization Tool on the Cell Broadband Engine and CUDA GPU

Contact Info

Product

Resources

About